The sizes of atmospheric particles and aerosols is important information in the atmospheric sciences. For example, the size of cloud vapor droplets, ice crystals, and aerosols are often needed to determine the properties and physics of thunderstorms. Data regarding atmospheric particle sizes is often collected from airborne instruments.
In previous measurement systems, particle size distributions have been estimated by measuring the sizes of individual particles. Once a sufficient number of particles are characterized, a histogram representing a particle size distribution is generated. This is problematic, however, because a large numbers of individual particle measurements are required. Acquiring the data needed to create a meaningful particle size histogram takes time and sometimes requires a long flight path for an airborne instrument, introducing potential errors into the data. Determining the sizes of individual particles is also a resource-intense activity that requires intensive real time processing capabilities to transmit, store, and process the required signals.
Previous particle measuring systems rely heavily on scattering models that make fundamental assumptions about particle properties such as the shape of a particle in making estimations. Moreover, the fundamental data unit that these systems approximate, individual particle size, is not useful; it is the distribution of particle sizes that is most important in the atmospheric sciences.
For example, forward scattering spectrometer technology is often used to estimate particle sizes. Mounted on the wing of an aircraft, forward scattering spectrometers focus laser light in the free stream outside the aircraft. Using a very small sample volume to avoid detecting more than one particle at a time, and assuming that particles are spherical in size, particle size is estimated based on the amount of forward scattered light. However, the error in particle size estimated increases substantially when particles are greater than 50 um in diameter, because the forward scattering spectrometer probe becomes sensitive to slight deviations in the probe's optical properties. Moreover, the forward scattering technique cannot be effectively applied to non-spherical particles because it is impossible to know what particle shape properties should be used. There is further uncertainty in sample volume because the sample volume of the probes is controlled using a focused laser and aperturing on the detector side, and this contributes to further error.
Optical array probes (OAP) are also frequently used to estimate particle sizes. Using a linear array of photo diodes illuminated by a laser, OAP form shadow images of particles. As a particle passes through the laser, it shades different diodes and a 2D image can be formed by combining successive readings of the detector array. The state of the photo-diodes are conventionally single bit (shaded or not with a threshold at 50%), although newer probes have added as many as three levels of shading. OAPs suffer from several issues. When a particle is not exactly in the object plane of the optical system the recorded image is defocused, resulting in overestimating particle size until the particle is sufficiently defocused so that the detectors do not detect the particle at all. Because the point of defocus depends on particle size, the sample volume of the probe depends on the particle size. However, determining particle concentrations using OAP requires both that the particles are sized correctly and that the sample volume of the particular particle is accurately known. In addition these issues, estimating the size properties of non-spherical particles is problematic with OAPs.
What is needed is a method to estimate a particle size distribution that requires no scattering models or assumptions about the particle properties to relate observed optical signals to particle physical properties. What is also needed is a method to determine the properties of a population of events from an ensemble measurement that has low computational system requirements, does not require extended data collection, and does not include assumptions that cause further uncertainties.